Imaging assisted scanning spectroscopy for gem identification

ABSTRACT

Systems and methods here may be used for automated capturing and analyzing spectrometer data of multiple sample gemstones on a stage, including mapping digital camera image data of samples, applying a Raman Probe to a first sample gemstone under evaluation on the stage, receiving spectrometer data of the sample gemstone from the probe, automatically moving the stage to a second sample, using the image data, and analyzing the other samples.

CROSS REFERENCE TO RELATED APPLICATION

The application claims priority to U.S. Provisional Application No.63/001,064 filed on Mar. 27, 2020, the entirety of which is herebyincorporated by reference.

FIELD

The field includes systems and methods for automated alignment ofinstruments used for analyzing a diamond or other gemstone, analyzinggemstones using spectroscopy, and/or analysis of digital images.

BACKGROUND

Raman/photoluminescence and absorption spectroscopies are tools used forgemstone identification and screening. For example, the systems andmethods may be used for screening lab-grown diamonds and diamondsimulants from natural diamonds as well as identifying the type of themineral in a gemstone.

Unfortunately, spectroscopy measurement requires accurate samplealignment to overlap the tested sample with the focal spot of theoptics. Failure to achieve the accurate alignment may result in weaksignal level or even reception of signals from neighboring samples on agiven piece of jewelry. And such alignment may be difficult due to useof a strong laser power and the reflected laser beam from polishedgemstones. Further complicating the matter, the lasers which are strongenough to generate Raman/photoluminescence signal are far beyondexposure limits for eye safety, so the entire measurement system and thesample may be enclosed in order to meet proper human safetyrequirements. Traditionally, such human users rely on visually aligningthe sample with a laser safety goggle and using the received signal as areference to back and forth align the sample until maximized the signal.This limitation slows down the alignment process and eliminatesautomated screening efficiencies.

An alternative method is to couple the spectroscopy system into anoptical microscope. Conventional microscope based Raman and absorptionspectroscopies with a motorized stage can be used for sample alignmentin a fully enclosed environment, however, an optical microscope hasrelatively small field of view. For example, a low magnification 4×objective lens may have only 2 mm horizontal field of view, which mightbe even smaller than the tested sample. When measuring multiple samples,such a small field of view may confine the eye sight of a user. Inaddition, jewelry identification does not need high spatial resolution(<10 μm) provided by the microscope. Each sample may only need one or asmall amount of well-aligned measurements. Therefore, using a microscopedoes not provide the advantage for gemstone screening either. Nor doescolor get evaluated in such systems.

There is a need for an automated system that allows for efficienttesting that is both accurate and able to be used in many differentcircumstances for multiple testing scenarios.

SUMMARY

Systems and methods here may be used to provide a method toautomatically align analysis tools for one or multiple gemstones, toanalyze gemstones in an easily reproducible arrangement and producesreliable results.

Systems and methods for capturing and analyzing spectrometer data onmultiple sample gemstones, may include using a computer with a processorand memory, in communication with a digital camera, a Raman probe, andat least one stage motor configured to move a stage, determining if afirst sample gemstone on the stage is in focus for the digital cameraand the Raman probe, by analyzing a captured digital image of the firstsample taken by the digital camera. In some examples, additionally oralternatively, the at least one stage motor is capable of moving thestage in an X, Y and Z direction, and rotating the stage. In someexamples, the Raman probe is mounted in an angled configuration, asmeasured from line of sight of the camera to keep out of thefield-of-view of the digital camera, if the sample is not in focus, bythe computer, focusing the camera on the sample by sending instructionto the motor to move Z stage, if the sample is in focus, capturing apixelated image of the stage using the digital camera, mapping multiplesamples including the first in the X, Y plane using the pixelinformation, determining, by the computer, relationships between thecaptured digital image pixels and distances of multiple samples on thestage, directing movement of the stage, by the stage motors, to positiona first sample under the Raman probe; and recording a spectrometersignal of the Raman probe for the first sample.

Additionally or alternatively, some examples further include determininga hue, lightness, and chroma value for the first sample using thedigital image pixels of the first sample, determining a color grade fromD to Z of the first sample, based on the corresponding hue, lightness,and chroma determined values. Additionally or alternatively, someexamples further include determining a size value for the first sampleusing the digital image pixels of the first sample, by comparing pixelnumbers in each sample to a calibration of distance.

Additionally or alternatively, some examples further include determininga size of the first sample by comparing pixel numbers in the firstsample of the pixelated image to a calibration of distance, determininga mineral type of the first sample based on the Raman spectra of thefirst sample, determining a density of the first sample using themineral type using a table of density and mineral types, determining avolume of the first sample using the determined size of the firstsample; and determining a weight of the first sample by multiplying thedetermined density by the determined volume.

Additionally or alternatively, systems and methods here for capturingand analyzing spectrometer data on multiple sample gemstones mayinclude, by a computer with a processor and memory, the computer incommunication with a digital camera, Raman probe, and stage motor, thecomputer determining if a sample on the stage is in focus for thedigital camera and Raman probe, if not, then calibrating, in someexamples calibrating includes conducting Z dimension alignment byadjusting the Z position of the stage using returns for a highest signalreturn, focusing the digital camera to a plane using sharpness of acaptured image, conducting a pixel-to-distance conversion factor betweendigital image pixels and actual distance using a known distance guide,and analyzing a captured image to locate a Raman probe laser spot, ifcalibrating is not necessary, or after calibrating, capturing a focusedpixelated image of samples on the stage, locating individual samples inthe X, Y plane using the pixel information, calculating the requiredmovement of the stage to place the Raman probe laser spot on a firstselected sample position using the pixel-to-distance conversion and thelaser spot information, sending commands to the stage motors for movingthe stage to position the first selected sample under the Raman probeand to overlap the first selected sample with the Raman probe laserspot, determining if the first selected sample is in focus by the camerafor analysis by the probe based on a pixelated image of the firstselected sample captured by the digital camera, if the pixelated imageof the first selected sample is determined to not be in focus, sendingcommands to the stage motors for moving the stage Z position,determining the pixelated image of the first selected sample is infocus, and recording a Raman probe spectrometer signal by a spectrometerfor the first selected sample.

In some examples, additionally or alternatively, after the spectrometersignal is recorded for the first selected sample, sending commands tothe stage motors for moving the stage to position a second selectedsample using the mapped coordinates and pixel-to-distance conversion.Some examples include determining if the selected second sample is infocus for the camera and probe based on a pixelated image captured bythe digital camera, if the pixelated image of the second selected sampleis determined to not be in focus, sending commands to the stage motorfor moving the stage Z position, determining the second selected sampleis in focus, and recording a Raman probe spectrometer signal by aspectrometer for the second selected sample.

Some examples include causing display of a result of the spectrometersignal of the first and second samples. And some examples includedetermining the pixelated image of the first selected sample is infocus, and recording a second Raman probe spectrometer signal by asecond spectrometer for the first selected sample, and in some examples,additionally or alternatively, the second Raman probe is mounted in anangled configuration as measured from a line of sight of the camera tothe stage, wherein the Raman probe and the second Raman probe are eachconfigured with spectrometers of different resolution, and wherein theRaman probe and the second Raman probe are each configured with lasersof different wavelengths. Some examples include causing display of aresult of the spectrometer signal of the first and second spectrometers.And in some examples, additionally or alternatively, the Raman probe ismounted in an angled configuration, out of a field-of-view of thedigital camera. And in some examples, additionally or alternatively, thedetermining if the first selected sample is in focus by the camera foranalysis by the includes sending instruction to the stage motor to movethe stage until the Raman probe returns a highest signal return for a Zdimension. In some examples, the Raman probe is mounted in an angledconfiguration, out of the field-of-view of the digital camera.

BRIEF DESCRIPTION OF THE DRAWINGS

For a better understanding of the embodiments described in thisapplication, reference should be made to the Detailed Description below,in conjunction with the following drawings in which like referencenumerals refer to corresponding parts throughout the figures.

FIG. 1 is an illustration of an example analysis system in accordancewith certain aspects described herein;

FIG. 2 is an example chart showing vertical pre-alignment examples inaccordance with certain aspects described herein;

FIG. 3 is an example chart showing horizontal pre-alignment examples inaccordance with certain aspects described herein;

FIG. 4 shows example wavelength and count charts in accordance withcertain aspects described herein;

FIGS. 5A and 5B are an example flow chart showing samples of the methodsteps that may be employed using the systems described herein;

FIG. 6 is an illustration of an example analysis system in accordancewith certain aspects described herein;

FIG. 7 is an example three dimensional chart extrapolation in accordancewith certain aspects described herein;

FIG. 8 is an illustration of an example networked system in accordancewith certain aspects described herein; and

FIG. 9 is an illustration of an example computer system in accordancewith certain aspects described herein.

DETAILED DESCRIPTION

Reference will now be made in detail to embodiments, examples of whichare illustrated in the accompanying drawings. In the following detaileddescription, numerous specific details are set forth in order to providea sufficient understanding of the subject matter presented herein. Butit will be apparent to one of ordinary skill in the art that the subjectmatter may be practiced without these specific details. Moreover, theparticular embodiments described herein are provided by way of exampleand should not be used to limit the scope of the particular embodiments.In other instances, well-known data structures, timing protocols,software operations, procedures, and components have not been describedin detail so as not to unnecessarily obscure aspects of the embodimentsherein.

Overview

Systems and methods here may be used to pre-align one or multiplespectroscopy probes with a dual or multiple magnification imaging systemon a stage arranged with automated motors. Since such alignment may bebased on the imaging system as well as an image process algorithm, theentire system may be enclosed to meet any kind of human laser safetyrequirement and also provide a solution for automated alignment ofsample gemstones for accurate and speedy analysis.

The use of Raman spectroscopy is an effective method for identificationof materials such as gemstones. For example, one application may be todecipher nephrite jade, from the lesser valuable jadeite although thetwo might be indecipherable to the naked eye. Such analysis may alsohelp decipher jade, both imitation jade (i.e. other minerals altogether)and processed jade, in which the minerals are heated, chemicallyaltered, dyed or waxed. First, the stone may be analyzed by applying alaser signal to a stone using a Raman probe, and then recording thereflected signal with a spectrometer. For example, the Si—O—Si symmetricstretching feature in a spectroscopy chart may be located at slightlydifferent places in the materials, 675 cm−1 in nephrite and above 695cm−1 in jadeite. Based on Raman spectroscopy graph peak positionsNephrite features may include: 162*, 179, 223, 368*, 394*, 415*, 675,930, 1031, and 1058 cm⁻¹ (*weak features) and Jadeite features mayinclude: 205, 377, 436, 695, 993, and 1039 cm⁻¹. These are differentresults from Raman probe analysis using a high resolutionspectroscopy<20 cm⁻¹ equals to 0.5 nm when using 532 nm as laserwavelength and spectral resolution: <0.3 nm. These peaks might need ˜0.3nm spectral resolution and equal to ˜10 cm⁻¹ to resolve. And further, insome examples, a map of the stone may be useful as the two materialsjadeite and nephrite could co-exist in the same gemstone.

Generally, these recorded results may be compared to known spectrometerrecords of various stones and variations of stones to find a match foridentification purposes. Other gemstones besides jade may similarly beidentified using Raman spectroscopy.

But application of such a system was limited to manual application ofthe Raman probe until the systems and methods described here. Thesystems and methods described herein may be used to analyze manymultiple sizes and shapes of gemstones, including those mounted injewelry or other mounts that might be difficult to otherwise analyze. Insome examples, only portions of gemstone may be seen in a mounted pieceof jewelry, and other portions obscured. The system may therefore beversatile in that it may remove the requirement of disgorging a gemstonefrom a mount in order to properly analyze it. This saves time, money,potential damage, and effort in analyzing many multiple gemstones in amounted condition, without removal, and while in an ordinary state.Further, the automated movement of the samples on the stage under theprobe may protect humans from laser exposures.

The system arrangement as described herein may include one or multiplelong focal length spectroscopy probes to measure samples. Thespectroscopy probe may be one or more Raman and/or Photoluminescenceprobe(s). In some examples, additionally or alternatively, multipleprobes may be used at the same time. In examples with more than oneprobe, each may include different laser wavelengths or one may utilizean absorption spectroscopy probe.

In some examples, additionally or alternatively, the camera may bemounted such that the field of view is down onto the stage where thesamples are located, because the camera may be used to capture imagesused for positioning of the stage and/or Raman probe to gather data.Because the camera is positioned with a line of sight to the samples andstage, any probes may be positioned out of the way of the cameraline-of-sight. In some examples, additionally or alternatively, theprobe(s) may be tilted such that only a small mount, or no amount of theprobe appears in the camera line of sight. In some examples,additionally or alternatively, the probe may be positioned such that itsshadow may not appear in the camera line of sight. This may be arrangedeven though the focus point of the Raman Probe would overlap withfocusing plane of the camera—in some examples by tilting the probe asshown in FIGS. 1 and 6. In such a way, the probes may be mounted at anangle as measured from a line of sight of the camera to the stage, toallow for a camera to be mounted to view the sample stage and gemstonesunder analysis. The camera images may be used to confirm the samplepositioning on a stage, and view the samples, while the probes conductthe analysis.

Additionally or alternatively, automated movement of the sample stage,to ensure proper alignment of the probes may be configured using thesystems and methods described herein.

Example Alignment and Mapping

The systems and methods here may be used to localize the position of agemstone sample and to calculate the actual distances in horizontal andvertical directions need to be moved in order to align the sample to thespectroscopy probes for measurements. The imaging system may include oneor multiple cameras which perform two main functions: to localize thesample position on the horizontal plane and confirm the verticalposition of the sample is overlapped with the focal point of thespectroscopy probes.

The first function may utilize a wide angle imaging lens which hasenough field of view to cover all the samples. In some examples,additionally or alternatively, the field of view can be set as 30 mm by25 mm, which may be wide enough to cover most samples on a typicalstage. One example field-of-view for screening/scanning application mayat least cover around 20 mm field of view, which is <0.45× magnificationwhen using a ⅔ inch frame size camera.

Such an example imaging system may have low or no image distortionacross the entire field of view. A conversion factor may be calculatedbetween the pixel size in the imaging system and the actual distance inthe image plane using a stage micrometer. The stage micrometer may be apiece of glass with micrometer patterns, similar to a ruler, which maybe used for imaging system calibration.

The second function may utilize an imaging lens with short depth offocus, which is sensitive to minor vertical movements. In such examples,a lens with high magnification plus high F-number may be suitable forthis function. The imaging system may be one camera or multiple cameraswith different magnifications.

The horizontal movement may be calculated by a computer system using thecollected images from the wide field of view camera to align the testedsample with the spectroscopy probes. In such examples, horizontalmovement parameters may be sent to the motorized translation stage tomove the sample for alignment. After the initial horizontal alignment,the vertical axis movement may be calculated by the computer systemusing the images from the short depth of focus camera to overlap thetested sample with the focal plane of the spectroscopy probes. Thevertical alignment process may be similar to the conventional auto-focusfunction, which the imaging system scans across the vertical directionand capture multiple images to calculate a focused vertical position. Anauto-focus algorithm may use the cut or the surface feature from thegemstone to confirm the focusing. The translation stage may then bemoved for the sample to the in-focus vertical position. It is possiblethat after vertical positioning, another horizontal alignment or backand forth horizontal and vertical alignment may be utilized. In someexamples, additionally or alternatively, one camera may be used insteadof two.

Once in alignment, the spectroscopy probes may measure the testedsamples by collecting Raman and/or Photoluminescence spectra using thespectrometer. The process from alignment to measurement may be repeatedfor each individual sample. Based on the result from the spectroscopyprobes, the user or the computer algorithm analyzes the spectra to makedecision for gemstone screening and identification. In some examples,additionally or alternatively, this may include chart analysis andmatching with known charts for other gemstone samples.

In some examples, additionally or alternatively, multiple Ramanspectroscopy probes can be integrated into one system to measure thesamples under different laser excitation wavelengths and/or usingdifferent field-of-views with the same setup.

Hardware Setup Examples

FIG. 1 shows an example hardware setup of the equipment which may beutilized to employ the methods described herein. As shown in FIG. 1, thefield of view 132 for the camera 115 may allow for alignment of theprobe 110 on the gemstone(s) 106 arranged in/on the stage 108. In someexamples, additionally or alternatively, the camera 115 may include alens 119. In some examples, additionally or alternatively, as discussedabove, the camera 115 lens 119 may be an Imaging lens for example, butnot limited to a Fixed magnification imaging lens, Macro lens (for lessdistortion), Telecentric lens (for long working distance), Manually ormotorized adjustable magnification imaging lens (for changing field ofview). The imaging lens 119 may also include manual or motorizedfocusing such as, but not limited to a digital single-lens reflex camera(DSLR).

The operator may simply place any number of sample gemstones 106 inholders or without holders on the stage 108 for analysis, and then movethe table stage 108 and/or the probe 110 to analyze the gemstones 106that may be arranged on or in the stage 108. The arrangement in FIG. 1may allow for automated alignment of many multiple samples and greatlysimplifies the process for the operator, who otherwise would have toload a new gemstone 106 for analysis one at a time, and align the probe110 manually, for each different stone sample.

In the example, many multiple component parts may be included into oneoverall unit. This unit may include a camera arrangement 115, a mountedRaman probe 110, and a gemstone stage 108 with accompanying motors 150along with a cover to shield the laser light from an operator user (notpictured). In some examples, additionally or alternatively, the motors150 may be servo and/or stepper motors, servo motors, AC servo motor, ACinduction motor, Piezo motor, Voice coil motor, and/or Actuator or anyother kind of electric or other motor capable of moving the stage in theX, Y, and/or Z dimensions 166 and/or rotating 152 about one or multipleaxes. In some examples, additionally or alternatively, each of thesecomponent parts may be mounted to an overall system frame (not shown) bymovable and/or adjustable and/or motorized mounting brackets and joints.In such a way, the X, Y, and Z 166 positions and tilt angles for eachcomponent part (camera 115, Raman Probe 110, stage 108, etc.) may bemoved independently from one another and/or rotated as needed to align,focus, and/or otherwise analyze the samples 106. In some examples,additionally or alternatively, such motors may be in communication withthe computing system to create a feedback loop for auto focus of thecameras, movement between samples, and automated analysis using imageanalysis.

In such examples, each of these component parts (Raman probe 110,spectrometer 117, laser 113, camera 115, table 108 motors 150,) may bein communication with a computer or computer systems such as thatdescribed in FIG. 9 (but not shown in FIG. 1). In this way, a singlesystem may house the camera, Raman probe(s), and movable stage that maybe useful in analyzing the gemstones 106 as described herein.

The camera 115 may capture image data that may be processed by acomputing device also in communication with motors on the stage 108 toadjust alignment in X, Y, and/or Z positions of the sample(s) asdescribed herein. Such image capture information may be sent to thecomputing system (not shown) for analysis as described herein. Further,such image data may be utilized to focus the images using Z movement ofthe table 108 by the motors 150, and to move to align the samples 106with the probe 110 in X and Y directions as described.

In some examples, additionally or alternatively, the stage 108 is ableto move using translation stepper motors and/or servo motors such thatthe probe 110 is fixed. In some examples, additionally or alternatively,the camera 115 may be focused on the stage 108 and/or samples 106 toensure the images captured are clear. This arrangement may allow thesystem to be pre-aligned to a focus plane and the probe 110 may then bepositioned so that everything on the stage 108 is in focus as describedherein.

The sensor used to measure the gemstones 106 may be a Raman and/orphotoluminescence probe 110. In the example of FIG. 1, the Raman probe110 includes fiber lines which include both the excitation lasertransmission lines 112 in communication with a laser 113 and collectionlines 116 for the spectrometer 117. The probe 110 may emit laserradiation 114 used to excite a gemstone 106 as described herein. In someexamples, additionally or alternatively, the light source 114 may be inthe visible, near infrared, or near ultraviolet range. Each wavelengthhas its own uses. For example, a 405 nm Raman/Photoluminescence probemay be appropriate for diamond screening, while a 532 nm or 785 nm Ramanprobe may be useful for colored gemstones identification. Other commonlyused wavelengths may also be included or utilized, for example, 445,488, 514.5, 633, 639, 660, 690, 730, 808, 830, 852, 975, and/or 980 nm.In examples with multiple Raman probes, each would utilize its own laserand spectrometer.

Because of the arrangement of the camera 115, the probe 110 may get inthe way of the image line of sight 132 of the camera 115. In someexamples, additionally or alternatively, the probe 110 may be mounted111 at an angle 140 at which the Raman probe 110 may still affect thesample 106 and receive a strong enough signal in response, but stay outof the way of the camera 115. In such examples, the angle 140 of theRaman probe 110 to the line of sight 132 of the camera 115 to the stage108 and gemstone samples 106 may allow the Raman probe 110 to not becaptured in any images taken by the camera 115 but still analyze thegemstones 106. In some examples, additionally or alternatively, a shadowarea of the probe 110 may be reduced in such a way. Further, sucharrangement may reduce the signal level only slightly from a straightdown angle and still be strong enough for analysis as described.

In some examples, additionally or alternatively, such a Raman probe 110may be set on a gimble, a hinge, a rotatable axis, rotating servo and/orstepper motor, or other movable arrangement 111 to allow for positioningand movement of such position including angle 140. In some examples,additionally or alternatively, the probe 110 may be placed/maneuvered inrelation to a target gemstone 106 by a human operator. In some examples,additionally or alternatively, a motor in communication with a computingsystem may be mounted 111 such that a pivoting motor may adjust theangle 140 automatically, and/or in response to using a computer program,and/or remotely, in response to a human operator sending instructions,or a combination of automated and/or manual adjustments. Such an anglemay be set between 15 to 30 degrees angle and in some examples,additionally or alternatively, 20 degrees down and toward the samplearea, as measured from the vertical Z axis. The selected pivot angle 140of the Raman probe 110 may depend on the viewing angle of the camera,the focal length and the dimension of the probes. The selected angle 140of the Raman probe 110 may be adjusted as described herein.

In some examples, additionally or alternatively, the X, Y and/or Zdistance of the Raman probe 110 in relation to the stage 108 and/orsample 106 may be adjusted by servo and/or stepper motors for the stage108 and/or probe 110 mount. In some examples, additionally oralternatively, such adjustments may be made by a computer system incommunication with the motors 150, and/or 111 as described herein. Insome examples, additionally or alternatively, such motors incommunication with the camera data analysis may provide a feedback loopusing image analysis and position of the probe 110, and table 108 asdescribed in more detail in FIG. 2 and FIG. 5.

This camera 115 may then digitally capture the images of the gemstone(s)106 for alignment as described herein. Such an image may includepixelated data representing the gemstone image as described herein. Thecameras 115, may include computer components and may also be incommunication with other computer components as described herein forprocessing the pixelated digital images, for saving, storing, sending,or otherwise aligning or manipulating the pixelated digital images ofthe gemstone tables.

In some examples, additionally or alternatively, the camera arrangement115, may be adjustable to adjust focal length, it may be fixed, orremovable. In some examples, additionally or alternatively, a lightsource such as panels fitted with and/or otherwise including LightEmitting Diodes (LEDs) 128 may surround, partially surround,approximate, or be near the stage 108 so as to aid in illuminating thegemstones 106 and aid the camera 115 with image capture for alignment.

In such examples, the lighting environment on the stage 108 may helpemphasize any color differences of gemstone samples 106. Homogeneous,diffused white light may help reduce any dark areas inside the gemstonesin captured images. As such, additionally or alternatively examples hereinclude different configurations of side panels fitted with and/orincluding LEDs 128 and optional top 160 and/or bottom 161 reflector(s)as described herein. Such a reflector could be any number of panels madeof, and/or coated with a light reflective material, such as but notlimited to metals such as aluminum, steel, copper, chromium, nickel,and/or any other combination of metals. In such examples, glass mirrorsmay be used as reflectors. Any combination of reflective materials thatare configured to reflect light, such as the light from the LED sidepanels 128 may be used. These illumination arrangements may allow for asprecise color measurements of the samples 106 as possible.

In some examples, a diffuser may be placed in front of the LED panel(s)128 to diffuse the light. In some examples, one, two, three, and/or fourLED panels 128 may be used to illuminate the stage 108 and samples 106.Although not depicted in FIG. 1, in some examples, these LED panels 128may surround the stage 108 on two, three, or four sides.

In some examples, the LED panel(s) 128 may be 200 mm width by 100 mmheight in size. In some examples, the LED panel(s) 128 may be between150-250 mm width by 50-150 mm height in size. Any combination of sizedpanels may be used.

In some examples, a reflector 160 may be positioned above, and/or below161 the stage 108. In examples where a reflector 160 is positioned abovethe stage 108, a hole 162, opening, and/or aperture may be made in thereflector in order for the camera 115 to view the stage 108 and samples106. In such examples, the reflector(s) 160, 161 may be made of anylight reflecting material and may be positioned such that the light fromthe LED panels(s) 128 are reflected toward the stage 108 and samples106. In some examples, panel(s) of LEDs 128 instead of reflectors 161,160 may be positioned above and/or below the stage 108 and samples 106.Any combination of panels of LEDs, and/or reflectors may be used toilluminate the stage 108 and samples 106. In such examples, a lightingenvironment with four side LED panels and both a top and bottomreflector can minimize dark area and emphasize the color differences inthe samples.

In examples where the panels of LEDs128 surround the stage 108 on foursides thereby forming a box, the Raman probe 110 may be positioned toreach into such box to analyze samples 106.

In some examples, additionally or alternatively, multiple cameras withmultiple fields of view may be utilized to better align the samples 106on the stage 108. In some examples, additionally or alternatively,multiple cameras with different fields-of-view may be used. For example,a beam splitter may be used to allow multiple cameras to share a singlefield of view 132 of the stage. For example, in a two camera system, abeam splitter may be designed to split a signal so each camera may viewthe same area. In some examples, additionally or alternatively, the beamsplitter could be a 50/50 or 80/20 depending on the application and thebrightness of the signal and signal wavelength.

It should be noted that the example of LED lights is merely an exampleand not intended to be limiting in any way. Any number of lightarrangements could be used to provide illumination on the stage andsamples, LEDs being just one example, alone or in combination such ashalogen, fluorescent, incandescent, and/or any other kind or type.

Vertical Pre-Alignment Examples

As may be utilized for alignment of the samples in the horizontal X andY directions, so too with vertical pre-alignment which may be calculatedto obtain acceptable focus of the samples for proper alignment and alsoanalysis measurements of samples, with the sample stage in an acceptablevertical Z dimension. To determine what is an acceptable vertical Zdimension for a sample, observations may be made, and then utilized toposition the stage in the Z direction, for example, a relative distance,or Z distance between the portion of the sample which is to be analyzedand the probe and camera.

In some examples, additionally or alternatively, samples may be placedon a stage with different relative heights above that stage. In someexamples, additionally or alternatively, multiple samples may berelatively the same height. This may result in different focus distancesfor the camera and the probe on these different samples. In someexamples, additionally or alternatively, an initial calibration baseposition of the camera and/or Raman probe may be found for each sample.In such examples, a Z position for the stage may be found that providesa focused view of the sample, and a focused reading by the probe.

For example, the stage may move to a new sample and due to the relativeheight of that sample on the stage, the camera distance from the lastsample may result in an out-of-focus view of the new sample. In suchcircumstances, the system may interpret the out-of-focus image as atrigger to reposition the stage and thereby focus the camera and allowthe probe to take a proper measurement. Such a movement may be based onsignal return strength as discussed in FIG. 2. In examples describedherein, the stage is usually moved in order to focus the camera and/orprobe for alignment and sample gathering. In some examples, additionallyor alternatively, however, the cameras may be moved and/or the probe maybe moved to focus the systems. Examples describing the sample stagemoving are not intended to be limiting, and other movable mounts withservo and/or stepper motors may be utilized.

FIG. 2 shows an example of vertical pre-alignment analysis chartedsignal strength 202 in relation to vertical Z position 204. In someexamples, additionally or alternatively, the image processing mayrequire about 0.25 mm of accuracy to properly capture an image to beused for analysis. In some examples, additionally or alternatively, thedepth of focus should be less than 0.25 mm. And because some cameraimaging may have +/−1 mm depth of focus, a further magnification of Zfocusing may be used.

FIG. 2 is an example chart showing vertical pre-alignment examples, thatis, vertical distance between the probe and a particular sample, inaccordance with certain aspects described herein. FIG. 2 showsPhotoluminescence Intensity (Counts) 202 as the Y axis, where signallevel used as reference to evaluate signal strength. The Z position inmillimeters of the probe in relation to the table is shown on the X axis204. As can be seen from the chart, there are Z positions for the probethat produce stronger signal strength, for example at what could becalled a sweet spot 210 the highest signal strength shown over a rangeof Z distances. Likewise, weak signals 220 may be detected at other Zdistances. Such an observation over a range of Z distances may allow forthe system to calibrate a Z setting of the stage that produces thestrongest signal strength to analyze.

After the Z position is finalized based on the method described in FIG.2, the camera position may be adjusted to make the image of the sampleas sharp as possible in terms of focus. Computerized image analysis maybe used to make this determination of the pixelated images. This is tooverlap the focus of the Raman probe an d the camera. In such examples,the focus of the camera image may be used to evaluate the quality of thefocus of the Raman probe.

For example, by only changing the Z distance from the probe to thetable, a chart like this may be made for a set of samples. Once thehighest signal strength 210 is observed, the rest of the samples may betaken at or near that observed Z distance. Additionally oralternatively, in some examples, additionally or alternatively, a Zdistance may be analyzed independently for each sample in turn.

Horizontal Pre-Alignment Examples

The spectroscopy probes may be pre-aligned with the imaging system tominimize time and effort during sample analysis, and allow for automatedmovement between samples for analysis. In arrangement such as FIG. 1,the probe 110 may be stationary, but the stage 108 may be movable bymotors 150 and/or human operators. For automated stage 108 movementexamples, each sample on a stage 108 may need to be positioned under theprobe 110 for analysis and signal gathering, one at a time. Suchmovement may utilize image data captured by the camera 115 to map thesamples. Further, in some examples, additionally or alternatively,information about the beam size and/or center may be used for analysis,and the servo and/or stepper motor 150 size to automate the movement ofthe table 108 to progress through the samples 106 and analyze each inturn. FIG. 3 is an example chart showing top-down horizontal X, Ypre-alignment examples in accordance with certain aspects describedherein. Such an arrangement may allow for the system to align the Ramanprobe at successive samples for spectra mapping signal analysis.

In FIG. 3 a horizontal X and Y top down view of a stage of samples isshown 302. As described, a relationship between the actual samples onthe stage 302 and an image 304 taken by the camera, may be used to movethe stage 302 to place different samples under the beam spot 306 of theprobe for analysis (110 in FIG. 1). In order to utilize such arelationship, the distances between the real world stage 302 andpixelated image data 304 may be translated by the computing system. Theimage 304 may be digital and made up of pixels to be correlated to beable to be used by counting them to find the actual distances andcoordinates of the stage 302. Such correlation may include convertingeach pixel in the image 304 into a real world distance. In someexamples, additionally or alternatively, each pixel in the image may be9.2 μm. In some examples, additionally or alternatively, each pixel maybe between 9 and 10 μm. In such a way, the coordinates of the stage 302and image 304 may be mapped to one another. Other factors of conversionmay include the magnification of the camera lens. In some examples,additionally or alternatively, as described above, a stage micrometermay be a piece of glass with micrometer patterns, and/or a ruler may beplaced on the stage, indicating known lengths, and an image taken of thestage with the ruler for the computer system to analyze the image tocount number of pixels in that particular arrangement that make up theruler length. For example, in some examples, additionally oralternatively, a 1 mm ruler in the real world X.Y on the stage maytranslate to 110 pixels in length on the image captured by the camera.By counting the pixels that span the designated, known length, aconversion factor may be determined and used as described herein.

After the image pixel relationship is determined, the samples within thecamera field of view on the stage 302 may be mapped by the computingsystem. But to perform an automated focus and/or sample progression, thesystem may also utilize the relationship between the movement of thestage 302 by servo and/or stepper motors (150 in FIG. 1) and a beam spot306 size generated and used by the probe 110 in FIG. 1 used foranalysis. In some examples, additionally or alternatively, the laserspot 306 may be fixed, and the stage moved to position the varioussamples under the laser for analysis. And if the conversiondetermination is used to know if the stage moves a certain number ofpixels, that translates to some real world length on the stage. This toomay allow for the system to move between samples to generate analysis ofeach sample in an automated way. In some examples, additionally oralternatively, an image may be mapped such that the system may be usedto pre-select positions of the samples for analysis before analysisbegins, and then an automated program may be used to move the stage asdetermined, using the pixels and determined conversion.

An example laser beam 306 spot size may be between 50 to 100 μm. In someexamples, additionally or alternatively, the spot size may depend on thefiber core diameter (112 in FIG. 1) as well as the focal length of theoptics in the probes (110 in FIG. 1). In some examples, additionally oralternatively, beam spots may be between 0.1 mm to 0.2 mm in diameter.In some examples, additionally or alternatively, the beam spot may bebetween 0.05 mm and 2.5 mm in diameter. In some examples, additionallyor alternatively, the servo and/or stepper motors on the stage 302 stepresolution may be below 10 μm. In such examples, the spot size maydominate the resolution analysis because many motor steps may be madewithin the same distance of one beam spot 306 size.

The system may use the mapped, pixelated image data of the stage 304 andthe size and/or center of the determined beam spot 306 as well as theservo and/or stepper motor incremental step sizes, to then move thestage such that each sample on the stage 302 is analyzed in turn.

Analysis Examples

The sample stage 302 labels six different examples for which Raman probeanalysis are shown in FIG. 4. One example method may use the setupdescribed here to screen target gemstones such as diamonds to determinewhether the diamond is synthetic. Such analysis may be used to detectdiamond overgrowth on a natural diamond and detecting the absence ofsuch overgrowth on a synthetic diamond. In such examples, thefluorescence spectrum or fluorescence image may be used to detectovergrowth. Such analysis may also include comparison analysis betweensaved images and captured images and/or to compare saved spectrum tocaptured spectrum as well.

The six samples include 360, 362, 364, 366, 368 and 369 which can bemeasured by both their [pixel coordinates] in an image and (X-Y) stageposition coordinates in the actual device, for example:

-   360, a. sample pixel coordinates [147,326] X-Y (20.37,6.10)-   362, b. sample pixel coordinates [170,1566] X-Y (20.16,17.51)-   364, c. sample pixel coordinates [1083,1242] X-Y (11.76,14.53)-   366, d. sample pixel coordinates [1219,794] X-Y (10.51,10.41)-   368 e. sample pixel coordinates [2046,337] X-Y (2.90,6.20)-   369 f. sample pixel coordinates [2320,1017] X-Y (0.38,12.46)

FIG. 4 shows the spectrometer graphs charting PhotoluminescenceIntensity (Counts×10⁴) against Wavelength absorption in nanometers (nm)for the six different samples from the table of samples in FIG. 3. Insuch a way, the various samples are analyzed, and correlated to theirposition on the sample stage such that they may be saved in the systemand identified such that their respective analyses results may bestored, sent, saved, compared, mapped, and/or otherwise utilized (usingcomputers such as those in FIGS. 8 and/or 9) for the respective sample(the six of FIG. 4 are merely illustrative and in no way limiting, inmany examples, each sample is analyzed). The example plots Wavelengthabsorption from 400 to 900 nm and counts from approximately 0 to 5×10⁴depending on the chart. The spectrometer results for the six exampleare:

-   460 a. sample is Cubic Zirconium-   462 b. sample is Moissanite-   464 c. sample is high pressure and high temperature (HPHT) lab grown    diamond-   466 d. sample is Natural Diamond-   468 e. sample is Natural Diamond-   469 f. sample is chemical vapor deposition laboratory grown diamond.

As can be seen, the charts depict different patterns ofPhotoluminescence Intensity (Counts×10⁴) against Wavelength absorptionin nanometers (nm) that may be used to identify a stone that is unknownand under analysis. Known charts may be used to compare to a new sampleanalysis chart for a matching comparison and identification.

Color Analysis Examples

In some examples, a color grade and/or determination may be made byanalysis of images captured of the sample gemstones. By analyzing thepixels of the samples, the systems here may be programmed to determineany combination of various characteristics such as, but not limited toconventional color space which includes three attributes: Lightness,Chroma, and Hue. This may be accomplished by the system's analysis ofproperly lit samples, and conversion of the red, green, and blue pixelassignments of the captured images of the samples.

For example, referring to FIG. 3, if three different samples 360, 366,368, may be analyzed by the system, and images captured of each. Sincethe images are pixelated digital images, the red, green, and blue colorscaptured in the images may be analyzed to determine attributes such asbut not limited to chroma, lightness, and hue. For example, hue may beconsidered the attribute of color perception by means of which a coloris judged to be red, orange, yellow, green, blue, purple, orintermediate between adjacent pairs of these, considered in a closering. Lightness, also referred to as tone may be the attribute by whicha perceived color is judged to be equivalent to one of a series of graysranging from black to white. Chroma, also known as saturation, may bethe attribute of color used to indicate the degree of departure of thecolor from a gray of the same lightness.

The color grading scale is from D to Z is shown in Table 1:

TABLE 1 D E F G H I J K L M N O P Q R S T U V W X Y Z Colorless NearFaint Very Light Light Colorless

From these determined values of chroma, lightness, and hue, colorestimates and/or grades may be assigned as shown in the example of Table2 below:

TABLE 2 Sample Lightness Chroma Hue Color grade 360 45.30 8.12 80.50 O/P(brown) 366 62.89 8.04 94.91 O/P 368 73.83 0.75 18.78 E

In such examples, pixels within the image may be subject to quantitativeanalysis. For example, each pixel can be analyzed to quantify the valuesof all color components in the particular pixel. The number of colorcomponent may be determined by an algorithm according to which the pixelis encoded when the color image is first captured. In some embodiments,the image is converted from its capturing color mode (e.g., CMYK) to adifferent color mode (e.g., RGB). After values are quantified for eachcolor component in each pixel, an average value can be calculated foreach color component in a given image. The process can be repeated forall images to calculate average value of each color component in allimages. Eventually, a final average value can be calculated for eachcolor component based on information from all images.

Using that information, the conversion process may be carried out forall pixels within a defined area in an image in order to calculateaverage values of the one or more parameters. The steps can be repeatedfor all images in the plurality of color images. Eventually, averagevalues of the one or more parameters (e.g., L*, a*, and b*) can becalculated for each color component based on information from allimages.

Next, a first score may be calculated based on the values of the one ormore parameters. For example, here the first score can be chroma (C*)and hue (h) values, calculated based on CIE color space values (e.g.,L*, a*, and b*); e.g., based on the following equations (FIG. 10):

$C*=\sqrt{{{{\left( a \right.{*)}}^{2} + \left( b \right.}{*)}}^{2}}$${h = {\tan^{- 1}\left( \frac{b*}{a*} \right)}}\mspace{20mu}$

In some embodiments, color images may be analyzed using the standards(e.g., tables of color matching functions and illuminants as a functionof wavelength) published by CIE. A plot of the standard daylightilluminant with a correlated color temperature of 6500 K, D₆₅. Thisilluminant may be represented here by the function H_(D65)(λ). The colormatching functions: x(λ), y(λ), z(λ) are used to calculate colorimetryparameters.

In some embodiments, the color grade represents the color or huecharacteristics of the body color of the sample gemstone.

Next, individual color components in each pixel within the physical areaof the gemstone in an image (e.g., defined by the corresponding outlinemask) are quantified. In some embodiments, each pixel is broken intothree values representing the colors red (R), green (G) and blue (B). Insome embodiments, each pixel is broken into three values representingthe colors cyan (C), magenta (M), yellow (Y) and black (K). In someembodiments, the image is converted from its capturing color mode (e.g.,CMYK) to a different color mode (e.g., RGB), or vice versa. Theindividual color components are then used to compute one or moreparameters, for example, CIE color space values (e.g., L*, a*, and b*).

Next, the one or more parameters (e.g., L*, a*, and b*) are computed forall images collected for a particular gemstone during one session (e.g.,under the same illumination conditions while the image capture component(e.g., a camera) is configured under the same setting.

An example of computing color characteristics (e.g., L*, a* and b*) isas follows. As diamond is a transparent material, the sum oftransmission spectrum T(λ) and reflection spectrum R(λ) is used in thecalculation of the tristimulus values, X, Y and Z:

X=Σ _(λ=380) ⁷⁸⁰ H _(D65)(λ)(T(λ)+R(λ)) x (λ)

Y=Σ _(λ=380) ⁷⁸⁰ H _(D65)(λ)(T(λ)+R(λ)) y (λ)

Z=Σ _(λ=380) ⁷⁹⁰ H _(D65)(λ)(T(λ)+R(λ)) z (λ).

The chromaticity coordinates, x and y, are then defined as:

${x = \frac{X}{X + Y + Z}}{y = \frac{Y}{X + Y + Z}}$

An attempt to achieve a “perceptually uniform”colour space is the CIE1976 colour space, otherwise known as the CIELAB colour space. Itsparameters are calculated from the tristimulus values as follows:lightness, L*=116(Y/Y_(W))^(1/3)−16

red-green parameter, a*=500[(X/X_(W))^(1/3)−(Y/Y_(W))^(1/3)]

and yellow-blue parameter, b*=200[(Y/Y_(W))^(1/3)−(Z/Z_(W))^(1/3)],

where X_(W), Y_(W) and Z_(W) are the tristumulus values for the whitepoint corresponding to the chosen illuminant, in this case D65.

$X_{w} = {{\sum\limits_{\lambda = {380}}^{780}{{H_{D65}(\lambda)}{\overset{¯}{x}(\lambda)}Y_{w}}} = {{\sum\limits_{\lambda = {380}}^{780}{{H_{D65}(\lambda)}{\overset{¯}{y}(\lambda)}Z_{w}}} = {\sum\limits_{\lambda = {380}}^{780}{{H_{D65}(\lambda)}{\overset{¯}{z}(\lambda)}}}}}$

The saturation or chroma is expressed as: C*_(ab)=(a*²+b*²)^(1/3) andthe hue angle is expressed as: h_(ab)=tan⁻¹(b*/a*).

Size/Weight Analysis Examples

In some examples, a size, and/or weight determination may be made byanalysis of images captured of the samples. By analyzing the pixels ofthe samples in the capture images, the systems here may be programmed todetermine any combination of various characteristics such as but notlimited to size of the sample gemstone(s) and weight of the samplegemstone(s).

In such examples, once a digital, pixelated image is captured by thesystem, an analysis of the image and pixels in the image may beanalyzed, counted, and compared to known values in order to determinefeatures such as size and/or weight. By using an edge detectionsoftware, the captured pixelated images may be analyzed to find theedges of each sample gemstone.

For example, in one image, once the edges of a sample gemstone areidentified, the system may count across the diameter of the gemstonetable. Such an image may be calibrated to known distances such that theimages may be compared to known size values. By counting the pixels, anddividing by the known calibrated numbers, a size may be determined, forexample, 129 pixels=1 mm. Such a calibration is not limited by thisparticular example and could be set to any pixel to distance ratiodepending on the camera arrangement and image analysis.

Additionally or alternatively, systems and methods here may be used forcalculating weight of a sample gemstone. In such examples, otherparameters may be used, in addition to the pixel counts used todetermine size. For example, spectra analysis may be used to determinethe mineral type in the gemstone, as described. With an assigned mineraltype, the system may determine a density for the sample gemstone. Insuch examples, a table may be used to determine a density for eachmineral type. Once determined both the size and density of the samplegemstone, the system may determine a weight of the sample using aformula.

Such a determination may be made by using the measured diameter or widthand length to estimate the volume, and the equation:

Weight=volume*density

In such examples, reference can be used, for example, a round shape 1ct. diamond is approximately 6.5 mm in diameter.

In one example, a 5 mm diameter diamond is approximately 0.46 ct. since(5/6.5){circumflex over ( )}3=0.455

For example, Table 3 below shows an example estimated weight for fivedifferent example gemstones:

TABLE 3 1 3.11 0.100 (SiC) 2 1.39 0.010 (Diamond) 3 1.95 0.027 (Diamond)4 1.73 0.019 (Diamond) 5 1.96 0.045 (CZ)

Stage Examples

Turning back to FIG. 1, in some examples, additionally or alternatively,the table or stage 108 upon which the sample gemstones are placed may beconfigured with or in conjunction with motors 150 that are capable ofmoving the stage 108 as described herein. In such examples, the stage108 may be able to maneuver in three directions or dimensions, X, Y, andZ 166 and/or rotating 152 about one or multiple axes. By moving thestage 108 in the X, and Y directions, the user and/or computer may beable to maneuver different sample gemstones 106 into the field of viewof the camera 115 for alignment, and angled probe 110 for analysis. Thismay allow for a large tray, platform, sample stage, or other gemstoneholding device to be analyzed at one time, by only moving the stage 108instead of swapping out samples one at a time. In this way, the numberof sample gemstones 106 that may be analyzed in one session may increaseand the amount of time to reload new samples is decreased thereby makingthe process of analyzing many multiple gemstones 106 more efficient.

By moving the stage 108 in the Z direction, the system may be able tofocus the camera 115 on the sample gemstone 106 currently in the fieldof view. This Z direction focus may be useful if the size and shape ofthe sample gemstones 106 are each different.

In some examples, additionally or alternatively, the motors 150 thataffect the movement of the stage 106 may be manually operated by a userwith switches and buttons. In such a way, a user may be able to load astage 108 with samples 106 and begin analyzing them by moving the tablein the X and Y directions.

In some examples, additionally or alternatively, the motors that affectthe movement of the stage 108 may be in communication with a computersystem (not shown) such as described in FIG. 9 and/or FIG. 8. In suchexamples, the computer may be programmed with the coordinates of thevarious sample gemstones 106 on the stage 108 and be able to maneuverthe motors to move the stage 108 in coordination with the camera 115 andthe Raman probe 110. In this way, the stage may be able to move quicklyafter the Raman probe 110 and the camera 115 effectuate the laser neededto test each sample gemstone 106 and then move to the next sample on thetable 108.

In some examples, additionally or alternatively, the camera setup 115may include computerized analysis of the pixelated images and includecommunication with the motors of the table 108. In such examples, afeedback loop may be created between the table 108 movement in the X, Y,and/or Z 166 directions, and data generated by the camera 115. In suchexamples, the table 108 may be maneuvered by the computers based on theanalysis of the images created of the gemstones 106 and instructionsprogrammed into the computer to focus images by moving the table in theZ direction for focused image capture. In some examples, additionally oralternatively, artificial intelligence or machine learning may be usedto help focus the samples in the Z direction. In such examples, manymultiple examples may be fed to the system, to train the algorithms sothat the algorithms used to determine if a sample is focused or not maylearn from the examples and over time make corrections to focus in the Zdirection to obtain better focused results. In some examples,additionally or alternatively, artificial intelligence and/or machinelearning may be used to help localizing samples in XY positions. Forexample, to locate each of multiple samples from the pixelated image andcalculate the corresponding stage movement to move sample under thelaser spot for analysis. Data may be fed into the algorithms to locatethe samples in the images for training the system to do soautomatically.

This computerized control of the stage 108 and camera system 115 mayimprove efficiency, speed up analysis, and accurately analyze multiplesamples 106 in one session.

Process Step Examples

FIGS. 5A and 5B depict an example flow chart of example steps that maybe used for image assisted analysis using the systems and methodsdescribed herein. In the process, the first step is for the system todetermine if a sample is in focus for the camera and probe, if not, thenthe system may need to calibrate 502. If calibration is unnecessary, thesystem may skip calibration steps and proceed with analysis of thesample step 510.

But if the system is to calibrate, the system would conduct Z dimensionalignment by adjusting the Z position of the stage using returns for ahighest signal return (See FIG. 2) and adjusting the camera to focus onthe same plane using the determined sharpness of the image 504. In someexamples, additionally or alternatively, sharpness of an image may bedetermined using an autofocus feedback loop between the camera image andcomputer analysis of the image to find clear image lines, boundarylines, etc.

Next to calibrate, the system may conduct a pixel-to-distance conversionby using an object with known size to calculate the conversion factorbetween pixel to actual distance 506. In such examples, a ruler or gridwith known distance may be placed before the camera on the stage so thecamera may capture an image of the ruler or grid and then the system maycount the number of pixels that fall between the known distance. In sucha way, the system may then retain the number of pixels per distance inorder to instruct the stage motors to move the samples.

Next to calibrate, the system may analyze a camera image to locate thelaser spot. This may be done by turning on the Raman probe laser andanalyzing the camera image to locate the center of the laser postposition based on the pixelated image 508. In some examples,additionally or alternatively, this may include an image analysisalgorithm to locate the bright laser spot, and determine a center tothat spot in the pixelated image. If calibration was necessary, thesesteps would conclude calibration of the system.

Once calibration is complete, or having already been calibrated, thesamples may be placed on the stage for analysis 510. Next, the systemmay capture a pixelated image of the samples, that have been focused bythe system and camera on stage 512. Next, the computing system mayautomatically locate the samples in the X, Y plane using the pixelinformation (See FIG. 3), or, by manual identification where the systemmay allow for a human use to select the sample of interested that needto be analyzed by the system 514. In manual identification, the systemmay allow a human user to select on a display the position on the samplethat the user wishes to have analyzed. Next, the system may calculatethe required movement of the stage to place the laser spot on the firstselected sample position using the pixel-to-distance conversion and thelaser spot information 516. Next, the system may move the stage toposition for mapped sample under probe to overlap it with the laser spot518. As explained, instructing the stage to move may include computerinstruction to an electric motor such as but not limited to steppermotors, servo motors, AC servo motor, AC induction motor, Piezo motor,Voice coil motor, and/or Actuator or any other kind of electric or othermotor capable of moving the stage.

Next, the system may determine if the selected sample is in focus forthe camera and probe based on an image captured by the camera 520. Next,if the image is determined by the system to not be in focus, the systemmay adjust the up and down or Z position of the stage to focus thesample and overlap the sample with the laser focal point for thesmallest laser spot 522. Next, once the sample is determined to be infocus, the system may record the Raman probe signal by the spectrometerfor the sample 524. After the spectrometer data is captured for thesample, the system may move the stage to the next sample using themapped coordinates and pixel-to-distance conversion 526. Finally, oncethe measurements have been captured for the given task, the system maycause display of the result of the sample or all of the samples 528 (SeeFIG. 4). In some examples, additionally or alternatively, the system mayrevert back to the first step to determine if a next sample is in focusfor the camera and probe, if not, then calibrate 502 to continue withthe steps as described.

In such a way, the system may automatically, using captured image data,computer analysis and method steps, process Raman analysis of a singlesample, and/or a set of samples on the stage without need for humaninteraction or input, or use little human interaction or input.

Multiple Raman Probe Examples

In some examples, additionally or alternatively, multiple Raman probesmay be used in the setup shown in FIG. 6 where the two Raman probes eachutilize different laser wavelengths for analysis but otherwise in asimilar setup to FIG. 1.

In some examples, additionally or alternatively, multiple Raman probesand the accompanying lasers and spectrometer may be tuned, configured,or otherwise built for a specific purpose, including complementarypurposes with multiple probes. The wavelength of the laser generated andprobe and the resolution of the spectrometer that analyzes the data maybe of different parameters that may be used for different purposes, fordifferent samples, etc. In such a way, both view range and resolutionmay be modified, tuned, or determined with multiple probes, fordifferent purposes, such as lower resolution and wider view range area,with another probe with higher resolution and smaller view range area.

For example, Raman spectroscopy with multiple Raman probes using a firstsetup: 405 nm laser, 400 to 900 nm wavelength range, 1.2 nm resolutionand a wide spectral range may be useful for diamond screening. Anothersecond setup may utilize: 532 nm laser, 532 to 670 nm range, 0.22 nmresolution with high resolution, which may be useful for gemstoneidentification.

Raman features may appear between 200 cm⁻¹ to 1500 cm^(−1,) which mayonly cover 41 nm in range but need high resolution spectrometer. Someminerals cannot be identified by a wide range 405 nm Raman/PL system dueto lower resolution and fluorescence background.

Spectrometer dependent resolution of 2000 pixels may be used to cover arange of area. An example of diamond screening using 400-900 nm=500 nmrange so each pixel is 0.3 nm and these 0.3 nm in the Raman Spectrum,resulting in a 70 scaled, but a lower scale of 10 or 5 may be useful toidentify small features, requiring higher resolution. Or in someexamples, additionally or alternatively, a specific region of a samplemay require analysis using a higher resolution than a wide spectralrange. In such examples, the two probe setups may complement one anotherwith lower resolution/higher range and higher resolution/lower range.

Another factor may be that different probes and/or lasers may utilizedifferent wavelengths. Some minerals may be better identified undercertain wavelengths. For example, if one wanted to measure Ramanspectrum of sapphire, it may be better to use 785 nm to avoidfluorescence and use a higher resolution. Another example may be 405 nmfor diamond analysis with low resolution but bigger range.

In some examples, additionally or alternatively, these two Raman probesmay focus on slightly different points on the sample and thereby eachgenerate its own chart (See FIG. 4). In some examples, additionally oralternatively, the multiple Raman probes may be time synchronized, suchthat each may emit its own laser beam and capture spectrometer data atdifferent times to avoid interference. In some examples, additionally oralternatively, no interference may be had and the two probes may eachanalyze slightly different parts of the same sample at the same time.

In FIG. 6, shows an example hardware setup of the equipment which may beutilized to employ the methods described herein with multiple probes. Inthe example, many multiple component parts may be included into oneoverall unit. This unit may include a camera arrangement 615, a firstRaman probe 610, a second Raman probe 690, each on their own pivotablemount angled to avoid the line of sight of the camera 615 and/or stepmotor 611, 691, each with corresponding fiber line 612, 692 to a laser613, 693 and a fiber line 616, 696 to a spectrometer 617, 697. Alsodepicted is a gemstone stage 608 along with a cover to shield the laserlight from an operator user (not pictured). In some examples,additionally or alternatively, each of these component parts may bemounted to the overall system frame (not shown) by movable and/oradjustable mounting brackets, joints, and/or motors. In such a way, theX, Y, and Z 666 positions for each component part (camera 615, RamanProbes 610, 690, stage 608, etc.) may be moved independently from oneanother and/or rotated as needed to align, focus, and/or otherwiseexcite and image capture the samples 606. In some examples, additionallyor alternatively, such motors may be in communication with the computingsystem to create a feedback loop for auto focus of the cameras, and toposition each successive sample under the probes for analysis.

The sensors used to measure the gemstones 606 may be theRaman/photoluminescence probes 610, 690 each in communication with aspectrometer 617, 697. The camera 615 may capture image data that may beprocessed by a computing device also in communication with servo and/orstepper motors 650 in, on, or around the stage 608 to adjust alignmentin X, Y, and/or Z positions and/or rotation 652 about one or multipleaxes of the sample(s) as described herein.

In some examples, additionally or alternatively, the camera 615 lens 619may be an Imaging lens for example, but not limited to a Fixedmagnification imaging lens, Macro lens (for less distortion),Telecentric lens (for long working distance), Manually or motorizedadjustable magnification imaging lens (for changing field of view). Theimaging lens 619 may also include manual or motorized focusing such as,but not limited to a digital single-lens reflex camera (DSLR).

Just as in the example of FIG. 1, the focus of the camera arrangements615 may be used in conjunction with the Raman probes 610, 690 tomaintain the working distance of the spectroscopy Raman probes 610, 690.But an advantage to the arrangement of FIG. 6, the multiple Raman probes610, 690 may each be in communication with a different lasers and/orlaser generators 613, 693 and/or own spectrometer, 617, 697, such thatdifferent analyses may be made of the samples 606 using one setuparrangement. As each Raman probe 610, 690 may be angled out of the wayof the camera 615, but still receive an acceptable signal return foranalysis by its own spectrometer, 617, 697, this multiple probearrangement may provide more accurate analysis using multiplewavelengths and thereby multiple tests on samples 606 in quick timesuccession. Additionally, each Raman probe 610, 690, may be set at theirown angle 640, 694 in relation to the samples 606 and camera view 632such that the individual Raman probes 610, 690 may best analyze thesamples 606 based on their laser 613, 693 wavelengths.

Local Mapping Examples

In other examples, the systems and methods here may be used to take onesample on a stone to store, compare, and otherwise analyze. But in someexamples, it may be advantageous to utilize the systems and methods hereto gather many multiple readings from the spectrometer on one samplestone, to more closely analyze that stone. In such a way, the system mayscan and locally map an area of an individual sample stone and use theincremental scanning position data and the spectrometer data to generatea three dimensional graph of the results over that given area. This maybe beneficial for samples with multiple characteristics within onestone, and/or for more valuable samples that may deserve more scrutinythan just one reading.

In examples with readings from one area of a stone, charts such as thoseshown in FIG. 4 may be generated using spectrometer analysis of a stone.But in examples where readings are taken over multiple X, Y horizontalpoints of one stone, such as those covering a two-dimensional surface,the systems and methods here may generate a three dimensional chart.Such a three dimensional chart may depict spectrometer data covering agiven two-dimensional area such as a table of a gemstone.

FIG. 7 shows just such an example, where a sample stone 702 has a givenarea on its table 710 where the system may scan over the surface toobtain results. For example, the area on the sample stone may be 5 mm×5mm square and the system may be programmed to scan that area in apattern, taking readings every pre-determined increment, such as but notlimited to every 100 μm in a scan pattern such as a grid, rows, columns,or other patterns. By scanning the two dimensional area, the system maycreate a three dimensional chart covering the two-dimensional area 720such as the 5 mm×5 mm area of the stone 702 of FIG. 7. In such examples,one spectrometer measurement is taken and recorded every predetermineddistance, such as but not limited to every 100 μm across thepredetermined pattern 720. The system may keep track of the positionwithin the two dimensional area where each reading is taken, and thereading results, and the data may be consolidated to create a chart withthree dimensions, X, Y, and wavelength as a 3-D model data set orresults 730. In such a way, the probe may be swept or scanned in apattern 720 over an entire predetermined two dimensional area 710 tocreate this three dimensional chart 730 which may be analyzed, compared,and used to determine information about the sample 702 at a moregranular level than sampling just one spot on the stone 702.

In some examples, additionally or alternatively, artificial intelligenceor machine learning may be used to help local mapping. In such examples,many multiple examples of incremental stage movement and/or imagefocusing may be fed to the system, to train the algorithms so that thealgorithms used to determine if a sample is focused or not, and maylearn from the examples and over time make corrections to obtain betterlocal mapping results.

Network Examples

Systems and methods here may utilize a networked computing arrangementas shown in FIG. 8. In FIG. 8, a computer 802 may be used to process thedata from the spectrometer (142 in FIG. 1), the pixel data of thecaptured images of the camera, send and receive instructions to thestage motors, or send and receive other data such as sample location,identification information of the stones, time and date, etc. Thecomputer 802 used for these steps could be any number of kinds ofcomputers such as those included in the spectrometer and/or cameraitself, and/or another computer arrangement in communication with thespectrometer and/or camera computer components including but not limitedto a laptop, desktop, tablet, phablet, smartphone, or any other kind ofdevice used to process and transmit digitized data. More examples aredescribed in FIG. 9.

Turning back to FIG. 8, the data captured for the pixelated image, stonesample identifying information, location, and/or spectrometer data fromwhichever computer 802 may be analyzed on a back end system instead ofor in addition to a local computer. In such examples, data may betransmitted to a back end computer 830 and associated data storage 832for saving, analysis, computation, comparison, or other manipulation. Insome examples, additionally or alternatively, the transmission of datamay be wireless 810 by a cellular or Wi-Fi transmission with associatedrouters and hubs. In some examples, additionally or alternatively, thetransmission may be through a wired connection 812. In some examples,additionally or alternatively, the transmission may be through a networksuch as the internet 820 to the back end server computer 830 andassociated data storage 832. At the back end server computer 830 andassociated data storage 832, the pixelated image data, sampleidentification, sample location, time, date, and/or spectrometer datamay be stored, analyzed, compared to previously stored spectrometer datafor matching, identification, and/or any other kind of data analysis. Insome examples, additionally or alternatively, the storing, analyzing,and/or processing of data may be accomplished at the computer 802 whichis involved in the original image capture and/or spectrometercollection. In some examples, additionally or alternatively, the datastoring, analyzing, and/or processing may be shared between the localcomputer 802 and a back end computing system 830. In such examples,networked computer resources 830 may allow for more data processingpower to be utilized than may be otherwise available at the localcomputers 802. In such a way, the processing and/or storage of data maybe offloaded to the compute resources that are available. In someexamples, additionally or alternatively, the networked computerresources 830 may be virtual machines in a cloud or distributedinfrastructure. In some examples, additionally or alternatively, thenetworked computer resources 830 may be spread across many multiplephysical or virtual computer resources by a cloud infrastructure. Theexample of a single computer server 830 is not intended to be limitingand is only one example of a compute resource that may be utilized bythe systems and methods described herein. In some examples, additionallyor alternatively, artificial intelligence and/or machine learning may beused to analyze the spectrometer data from the samples and/or focus theimaging camera for use with stage movement. Such systems may employ datasets to train algorithms to help produce better and better results ofanalysis of samples, identification of focused samples, stage movement,etc.

Example Computer Devices

FIG. 9 shows an example computing device 900 which may be used in thesystems and methods described herein. In the example computer 900 a CPUor processor 910 is in communication by a bus or other communication 912with a user interface 914. The user interface includes an example inputdevice such as a keyboard, mouse, touchscreen, button, joystick, orother user input device(s). The user interface 914 also includes adisplay device 918 such as a screen. The computing device 900 shown inFIG. 9 also includes a network interface 920 which is in communicationwith the CPU 920 and other components. The network interface 920 mayallow the computing device 900 to communicate with other computers,databases, networks, user devices, or any other computing capabledevices. In some examples, additionally or alternatively, the method ofcommunication may be through WIFI, cellular, Bluetooth Low Energy, wiredcommunication, or any other kind of communication. In some examples,additionally or alternatively, the example computing device 900 includesperipherals 924 also in communication with the processor 910. In someexamples, additionally or alternatively, peripherals include stagemotors 926 such as electric servo and/or stepper motors used for movingthe stage for the sample analysis. In some examples peripherals 924 mayinclude camera equipment 928, and/or spectrometer 929. In some examplecomputing device 900 a memory 922 is in communication with the processor910. In some examples, additionally or alternatively, this memory 922may include instructions to execute software such as an operating system932, network communications module 934, other instructions 936,applications 938, applications to digitize images 940, applications toprocess image pixels 942, data storage 958, data such as data tables960, transaction logs 962, sample data 964, sample location data 970 orany other kind of data.

Conclusion

As disclosed herein, features consistent with the present embodimentsmay be implemented via computer-hardware, software and/or firmware. Forexample, the systems and methods disclosed herein may be embodied invarious forms including, for example, a data processor, such as acomputer that also includes a database, digital electronic circuitry,firmware, software, computer networks, servers, or in combinations ofthem. Further, while some of the disclosed implementations describespecific hardware components, systems and methods consistent with theinnovations herein may be implemented with any combination of hardware,software and/or firmware. Moreover, the above-noted features and otheraspects and principles of the innovations herein may be implemented invarious environments. Such environments and related applications may bespecially constructed for performing the various routines, processesand/or operations according to the embodiments or they may include acomputer or computing platform selectively activated or reconfigured bycode to provide the necessary functionality. The processes disclosedherein are not inherently related to any particular computer, network,architecture, environment, or other apparatus, and may be implemented bya suitable combination of hardware, software, and/or firmware. Forexample, various machines may be used with programs written inaccordance with teachings of the embodiments, or it may be moreconvenient to construct a specialized apparatus or system to perform therequired methods and techniques.

Aspects of the method and system described herein, such as the logic,may be implemented as functionality programmed into any of a variety ofcircuitry, including programmable logic devices (“PLDs”), such as fieldprogrammable gate arrays (“FPGAs”), programmable array logic (“PAL”)devices, electrically programmable logic and memory devices and standardcell-based devices, as well as application specific integrated circuits.Some other possibilities for implementing aspects include: memorydevices, microcontrollers with memory (such as EEPROM), embeddedmicroprocessors, firmware, software, etc. Furthermore, aspects may beembodied in microprocessors having software-based circuit emulation,discrete logic (sequential and combinatorial), custom devices, fuzzy(neural) logic, quantum devices, and hybrids of any of the above devicetypes. The underlying device technologies may be provided in a varietyof component types, e.g., metal-oxide semiconductor field-effecttransistor (“MOSFET”) technologies like complementary metal-oxidesemiconductor (“CMOS”), bipolar technologies like emitter-coupled logic(“ECL”), polymer technologies (e.g., silicon-conjugated polymer andmetal-conjugated polymer-metal structures), mixed analog and digital,and so on.

It should also be noted that the various logic and/or functionsdisclosed herein may be enabled using any number of combinations ofhardware, firmware, and/or as data and/or instructions embodied invarious machine-readable or computer-readable media, in terms of theirbehavioral, register transfer, logic component, and/or othercharacteristics. Computer-readable media in which such formatted dataand/or instructions may be embodied include, but are not limited to,non-volatile storage media in various forms (e.g., optical, magnetic orsemiconductor storage media) and carrier waves that may be used totransfer such formatted data and/or instructions through wireless,optical, or wired signaling media or any combination thereof. Examplesof transfers of such formatted data and/or instructions by carrier wavesinclude, but are not limited to, transfers (uploads, downloads, e-mail,etc.) over the Internet and/or other computer networks via one or moredata transfer protocols (e.g., HTTP, FTP, SMTP, and so on).

Unless the context clearly requires otherwise, throughout thedescription and the claims, the words “comprise,” “comprising,” and thelike are to be construed in an inclusive sense as opposed to anexclusive or exhaustive sense; that is to say, in a sense of “including,but not limited to.” Words using the singular or plural number alsoinclude the plural or singular number respectively. Additionally, thewords “herein,” “hereunder,” “above,” “below,” and words of similarimport refer to this application as a whole and not to any particularportions of this application. When the word “or” is used in reference toa list of two or more items, that word covers all of the followinginterpretations of the word: any of the items in the list, all of theitems in the list and any combination of the items in the list.

Although certain presently preferred implementations of the descriptionshave been specifically described herein, it will be apparent to thoseskilled in the art to which the descriptions pertains that variationsand modifications of the various implementations shown and describedherein may be made without departing from the spirit and scope of theembodiments. Accordingly, it is intended that the embodiments be limitedonly to the extent required by the applicable rules of law.

The present embodiments can be embodied in the form of methods andapparatus for practicing those methods. The present embodiments can alsobe embodied in the form of program code embodied in tangible media, suchas floppy diskettes, CD-ROMs, hard drives, or any other machine-readablestorage medium, wherein, when the program code is loaded into andexecuted by a machine, such as a computer, the machine becomes anapparatus for practicing the embodiments. The present embodiments canalso be in the form of program code, for example, whether stored in astorage medium, loaded into and/or executed by a machine, or transmittedover some transmission medium, such as over electrical wiring orcabling, through fiber optics, or via electromagnetic radiation,wherein, when the program code is loaded into and executed by a machine,such as a computer, the machine becomes an apparatus for practicing theembodiments. When implemented on a processor, the program code segmentscombine with the processor to provide a unique device that operatesanalogously to specific logic circuits.

The software is stored in a machine readable medium that may take manyforms, including but not limited to, a tangible storage medium, acarrier wave medium or physical transmission medium. Non-volatilestorage media include, for example, optical or magnetic disks, such asany of the storage devices in any computer(s) or the like. Volatilestorage media include dynamic memory, such as main memory of such acomputer platform. Tangible transmission media include coaxial cables;copper wire and fiber optics, including the wires that comprise a buswithin a computer system. Carrier-wave transmission media can take theform of electric or electromagnetic signals, or acoustic or light wavessuch as those generated during radio frequency (RF) and infrared (IR)data communications. Common forms of computer-readable media thereforeinclude for example: disks (e.g., hard, floppy, flexible) or any othermagnetic medium, a CD-ROM, DVD or DVD-ROM, any other optical medium, anyother physical storage medium, a RAM, a PROM and EPROM, a FLASH-EPROM,any other memory chip, a carrier wave transporting data or instructions,cables or links transporting such a carrier wave, or any other mediumfrom which a computer can read programming code and/or data. Many ofthese forms of computer readable media may be involved in carrying oneor more sequences of one or more instructions to a processor forexecution.

The foregoing description, for purpose of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the embodiments to the precise forms disclosed. Many modificationsand variations are possible in view of the above teachings. Theembodiments were chosen and described in order to best explain theprinciples of the embodiments and its practical applications, to therebyenable others skilled in the art to best utilize the various embodimentswith various modifications as are suited to the particular usecontemplated.

What is claimed is:
 1. A method of capturing and analyzing spectrometerdata on multiple sample gemstones, the method comprising: by a computerwith a processor and memory, in communication with a digital camera, aRaman probe, and at least one stage motor configured to move a stage,determining if a first sample gemstone on the stage is in focus for thedigital camera and the Raman probe, by analyzing a captured digitalimage of the first sample taken by the digital camera, wherein the atleast one stage motor is capable of moving the stage in an X, Y and Zdirection, and rotating the stage, and wherein the Raman probe ismounted in an angled configuration as measured from a line of sightbetween the camera and the stage; if the first sample is not in focus,by the computer, focusing the camera on the first sample by sendinginstruction to the motor to move Z stage; if the first sample is infocus, capturing a pixelated image of the stage including first sampleand multiple samples using the digital camera; mapping the multiplesamples including the first sample in the X, Y plane using the pixelatedimage; determining, by the computer, relationships between pixels in thepixelated image and distances of the multiple samples on the stage;directing movement of the stage, by the stage motors, to position thefirst sample under the Raman probe; and recording a spectrometer signalof the Raman probe for the first sample.
 2. The method of claim 1,further comprising, determining a hue, lightness, and chroma value forthe first sample using the pixelated image of the first sample;determining a color grade from D to Z of the first sample, based on thecorresponding hue, lightness, and chroma determined values.
 3. Themethod of claim 1, further comprising, determining a size value for thefirst sample using the pixelated image of the first sample, by comparingpixel numbers in each sample to a calibration of distance.
 4. The methodof claim 1, further comprising, determining a size of the first sampleby comparing pixel numbers in the first sample of the pixelated image toa calibration of distance; determining a mineral type of the firstsample based on the Raman spectra of the first sample; determining adensity of the first sample using the mineral type using a table ofdensity and mineral types; determining a volume of the first sampleusing the determined size of the first sample; and determining a weightof the first sample by multiplying the determined density by thedetermined volume.
 5. The method of claim 1 further comprising,recording a second signal analysis of a second Raman probe for the firstsample, wherein the second Raman probe is mounted in an angledconfiguration as measured from a line of sight between the camera andthe stage, wherein the Raman probe and the second Raman probe are eachconfigured with lasers of different wavelengths.
 6. The method of claim1 further comprising, directing movement of the stage, to position asecond sample under the Raman probe; and recording a second signalanalysis of a second Raman probe for the second sample, wherein thesecond Raman probe is mounted in an angled configuration as measuredfrom a line of sight between the camera and the stage, wherein the Ramanprobe and the second Raman probe are each configured with spectrometersof different resolution.
 7. The method of claim 6 wherein, after thesecond sample is under the Raman probe, determining if the second samplegemstone on the stage is in focus for the digital camera and the Ramanprobe, by analyzing a captured image of the sample taken by the digitalcamera; and if the second sample is in focus, recording a signalanalysis of the Raman probe for the second sample.
 8. The method ofclaim 1 further comprising, by a back end computer with a processor anda memory, comparing the received signal analysis of the Raman probe ofthe first sample gemstone to known data of gemstones, and determining amatch of the received signal analysis of the Raman probe with the knowndata of gemstones.
 9. The method of claim 1 wherein directing movementof the stage includes determining relationships, by the computer,between a laser spot position of a Raman probe, and increments the stagemotors may move.
 10. The method of claim 1 further comprising, causingdisplay of a result of the spectrometer signal of the first sample. 11.The method of claim 1 further comprising, after recording a signalanalysis of the Raman probe for the first sample, directing movement ofthe stage, by the stage motors, to a second position on the first sampleunder the Raman probe; recording a second signal analysis of the Ramanprobe for the first sample; and recording additional signal analysis ofthe first sample over a predetermined area of the first sample.
 12. Themethod of claim 11 further comprising, causing display of a result ofthe additional signal analysis recorded for the first sample over thepredetermined area as a three dimensional plot.
 13. A method ofcapturing and analyzing spectrometer data on multiple sample gemstones,the method comprising: by a computer with a processor and memory, thecomputer in communication with a digital camera, Raman probe, and stagemotor, the computer determining if a first sample on the stage is infocus for the digital camera and Raman probe, if not, then calibrating,wherein calibrating includes, conducting Z dimension alignment byadjusting a Z position of the stage using returns for a highest signalreturn, focusing the digital camera to a plane using sharpness of acaptured image, conducting a pixel-to-distance conversion factor betweendigital image pixels and actual distance using a known distance guide,and analyzing the captured image to locate a Raman probe laser spot; ifcalibrating is not necessary, or after calibrating, capturing a focusedpixelated image of the first sample on the stage; locating additionalsamples in the X, Y plane using the focused pixelated image; calculatinga required movement of the stage to place the Raman probe laser spot onthe first sample and its corresponding position using thepixel-to-distance conversion and the laser spot ; sending commands tothe stage motors for moving the stage to position the first sample underthe Raman probe and to overlap the first sample with the Raman probelaser spot; determining if the first sample is in focus by the camerafor analysis by the probe based on a second pixelated image of the firstsample captured by the digital camera; if the second pixelated image ofthe first sample is determined to not be in focus, sending commands tothe stage motors for moving the stage Z position; determining if a thirdpixelated image of the first sample is in focus, and recording a firstsample Raman probe spectrometer signal by a spectrometer for the firstsample.
 14. The method of claim 13 further comprising, after thespectrometer signal is recorded for the first sample, sending commandsto the stage motors for moving the stage to position a second sampleusing the mapped coordinates and pixel-to-distance conversion.
 15. Themethod of claim 14 further comprising, determining if the second sampleis in focus for the camera and probe based on a fourth pixelated imagecaptured by the digital camera; if the fourth pixelated image of thesecond sample is determined to not be in focus, sending commands to thestage motor for moving the stage Z position; determining the secondsample is in focus, and recording a second sample Raman probespectrometer signal by a spectrometer for the second sample.
 16. Themethod of claim 15 further comprising, causing display of a result ofthe spectrometer signal of the first sample and second sample.
 17. Themethod of claim 13 further comprising, determining if a fifth pixelatedimage of the first sample is in focus, and recording a second Ramanprobe spectrometer signal by a second spectrometer for the first sample,wherein the second Raman probe is mounted in an angled configuration asmeasured from a line of sight between the camera and the stage, whereinthe Raman probe and the second Raman probe are each configured withspectrometers of different resolution, and wherein the Raman probe andthe second Raman probe are each configured with lasers of differentwavelengths.
 18. The method of claim 17 further comprising, causingdisplay of a result of the spectrometer signal of the first and secondspectrometers.
 19. The method of claim 13 wherein the Raman probe ismounted in an angled configuration as measured from a line of sightbetween the camera and the stage.
 20. The method of claim 13 wherein thedetermining if the first sample is in focus by the camera for analysisby the includes sending instruction to the stage motor to move the stageuntil the Raman probe returns a highest signal return for a Z dimension.21. The method of claim 13 wherein sending commands to the stage motorsfor moving the stage includes determining relationships, by thecomputer, between a laser spot position of a Raman probe, and incrementsthe stage motors may move.
 22. The method of claim 13, furthercomprising, determining a hue, lightness, and chroma value for the firstsample using the digital image pixels of the first sample; determining acolor grade from D to Z of the first sample, based on the correspondinghue, lightness, and chroma determined values.
 23. The method of claim13, further comprising, determining a size value for the first sampleusing the digital image pixels of the first sample, by comparing pixelnumbers in each sample to a calibration of distance.
 24. The method ofclaim 13, further comprising, determining a size of the first sample bycomparing pixel numbers in the first sample of the pixelated image to acalibration of distance; determining a mineral type of the first samplebased on the Raman spectra of the first sample; determining a density ofthe first sample using the mineral type using a table of density andmineral types; determining a volume of the first sample using thedetermined size of the first sample; and determining a weight of thefirst sample by multiplying the determined density by the determinedvolume.
 25. A system for recording spectrometer readings of multiplegemstone samples, the system comprising, a computer with a processor anda memory, in communication with a digital camera, at least one motorconfigured to move a stage, a Raman probe with a laser and aspectrometer, wherein the stage is configured to receive multiplegemstones for analysis by the spectrometer, wherein the digital camerais mounted with a field-of-view covering at least a portion of the stagewhere the multiple gemstones may be received, and wherein the Ramanprobe is mounted at an angle as measured from a line of sight betweenthe camera and the stage and aimed at least a portion of the stage. 26.The system of claim 25 further wherein the stage motor is configured tomove the stage in response to instructions from the computer havinganalyzed pixelated digital images from the digital camera to identifypositions of the multiple gemstones on the stage, such that a laser fromthe Raman probe is positioned on each of the multiple gemstones on thestage, successively, for analysis.
 27. The system of claim 25 furthercomprising four panel surrounding the stage, each panel including LightEmitting Diodes (LED) arranged to illuminate the stage, wherein the LEDsare configured to emit white light.
 28. The system of claim 27 furthercomprising a first reflector arranged above the stage and a secondreflector arranged below the stage.
 29. The system of claim 27 whereineach of the four panels of LEDs include a diffuser.